TY - CHAP A1 - Upcroft, Ben A1 - Ridley, Matthew A1 - Ong, Lee Ling A1 - Douillard, Bertrand A1 - Kaupp, Tobias A1 - Kumar, Suresh A1 - Bailey, Tim A1 - Ramos, Fabio A1 - Makarenko, Alexei A1 - Brooks, Alex A1 - Sukkarieh, Salah A1 - Durrant-Whyte, Hugh F. ED - Khatib, Oussama ED - Kumar, Vijay ED - Rus, Daniela T1 - Multi-level state estimation in an outdoor decentralised sensor network T2 - Experimental Robotics: The 10th International Symposium on Experimental Robotics ; Springer Tracts in Advanced Robotics (STAR, volume 39) N2 - Decentralised estimation of heterogeneous sensors is performed on an outdoor network. Attributes such as position, appearance, and identity represented by non-Gaussian distributions are used in in the fusion process. It is shown here that real-time decentralised data fusion of non-Gaussian estimates can be used to build rich environmental maps. Human operators are also used as additional sensors in the network to complement robotic information. Y1 - 2008 SN - 978-3-540-77456-3 U6 - https://doi.org/10.1007/978-3-540-77457-0_33 SP - 355 EP - 365 PB - Springer CY - Berlin, Heidelberg ER - TY - THES A1 - Kaupp, Tobias T1 - Probabilistic human-robot information fusion N2 - This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability. Y1 - 2008 UR - http://hdl.handle.net/2123/2554 ER - TY - CHAP A1 - Kaupp, Tobias A1 - Makarenko, Alexei A1 - Ramos, Fabio A1 - Durrant-Whyte, Hugh T1 - Human sensor model for range observations T2 - IJCAI Workshop Reasoning with Uncertainty in Robotics (RUR) at IJCAI'05 Y1 - 2005 ER - TY - CHAP A1 - Brooks, Alex A1 - Makarenko, Alexei A1 - Kaupp, Tobias A1 - Williams, Stefan A1 - Durrant-Whyte, Hugh T1 - Implementation of an indoor active sensor network T2 - Experimental Robotics IX: The 9th International Symposium on Experimental Robotics ; Springer Tracts in Advanced Robotics (STAR,volume 21) N2 - This paper describes an indoor Active Sensor Network, focussing on the implementation aspects of the system, including communication and the application framework. To make the system description more tangible we describe the latest in a series of indoor experiments implemented using ASN. The task is to detect and map motion of people (and robots) in an office space using a network of 12 stationary sensors. The network was operational for several days, with individual platform coming on and off line. On several occasions the network consisted of 39 components. The paper includes a section on the lessons learned during the project’s design and development which may be applicable to other heterogeneous distributed systems with data-intensive algorithms. Y1 - 2006 SN - 978-3-540-28816-9 U6 - https://doi.org/10.1007/11552246_38 SP - 397 EP - 406 PB - Springer CY - Berlin, Heidelberg ER - TY - JOUR A1 - Brooks, Alex A1 - Kaupp, Tobias A1 - Makarenko, A. A1 - Williams, S. A1 - Oreback, A. T1 - Software Engineering for Experimental Robotics JF - Springer Tracts Series in Advanced Robotics Y1 - 2007 VL - 30 SP - 231 EP - 251 PB - Springer CY - Berlin ER - TY - CHAP A1 - Brooks, Alex A1 - Kaupp, Tobias A1 - Makarenko, Alexei A1 - Williams, Stefan A1 - Orebäck, Anders ED - Brugali, Davide T1 - Orca: A component model and repository T2 - Software engineering for experimental robotics ; Springer Tracts in Advanced Robotics (STAR,volume 30) N2 - This Chapter describes Orca: an open-source project which applies Component-Based Software Engineering principles to robotics. It provides the means for defining and implementing interfaces such that components developed independently are likely to be inter-operable. In addition it provides a repository of free re-useable components. Orca attempts to be widely applicable by imposing minimal design constraints. This Chapter describes lessons learned while using Orca and steps taken to improve the framework based on those lessons. Improvements revolve around middleware issues and the problems encountered while scaling to larger distributed systems. Results are presented from systems that were implemented. Y1 - 2007 SN - 978-3-540-68949-2 U6 - https://doi.org/10.1007/978-3-540-68951-5_13 SP - 231 EP - 251 PB - Springer CY - Berlin, Heidelberg ER - TY - CHAP A1 - Endres, Felix A1 - Reinhart, Lukas A1 - Kaupp, Tobias A1 - Willert, Volker T1 - Perspektiveninvariante Inferenz von Eckpunkten in Packmustern von Kartonagen T2 - FORUM BILDVERARBEITUNG 2022 IMAGE PROCESSING FORUM 2022 Y1 - 2022 SP - 201 EP - 201 ER - TY - CHAP A1 - Wang, X. Rosalind A1 - Kumar, Suresh A1 - Kaupp, Tobias A1 - Upcroft, Ben A1 - Durrant-Whyte, Hugh ED - Sammut, C. T1 - Applying ISOMAP to the learning of hyperspectral image T2 - Australian Conference on Robotics and Automation (ACRA’05) N2 - In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed. Y1 - 2005 UR - https://eprints.qut.edu.au/40436/ SP - 1 EP - 8 ER - TY - CHAP A1 - Kaupp, Tobias A1 - Makarenko, Alexei A1 - Kumar, Suresh A1 - Upcroft, Ben A1 - Williams, Stefan T1 - Operators as information sources in sensor networks T2 - 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems N2 - This paper presents an approach of integrating human operators into a sensor network formed by a heterogeneous team of unmanned air and ground vehicles. Several objectives of human-network interaction are identified. The main focus of this work is on human-to-network information flow, i.e. human operators are regarded as information sources. It is argued that operators should make raw observations which are converted into the sensor network's common representation by a probabilistic model. The concepts are discussed in the context of an outdoor sensor network under development. Human operators contribute geometric feature information in the form of range and bearing observations. Visual feature properties are specified via meaningful class labels. A sensor model, represented as a Bayesian network, translates label observations into the system's representation. The model is also used to classify features as observed by robotic sensors. Y1 - 2005 U6 - https://doi.org/10.1109/IROS.2005.1545015 SP - 936 EP - 941 PB - IEEE ER - TY - CHAP A1 - Kaupp, Tobias A1 - Makarenko, Alexei T1 - Measuring human-robot team effectiveness to determine an appropriate autonomy level T2 - 2008 IEEE International Conference on Robotics and Automation N2 - This paper proposes a methodology to measure the effectiveness of a human-robot team as part of an adjustable autonomy system. The effectiveness measure is aimed at determining an appropriate autonomy level prior to the system's deployment. Two competing goals need to be traded off: maximising robot performance while minimising the amount of human input. The relative importance of the two goals depend on the mission priorities and constraints which are taken into account. The proposed methodology is applied to a human-robot communication system developed for task- oriented information exchange. The robot uses a decision- theoretic framework to act autonomously and to decide when to request input from human operators. The latter is achieved by computing the value-of-information an operator is able to provide which is compared to the cost of obtaining the information. For our system, the cost parameter represents the autonomy level to be determined. We demonstrate how an appropriate autonomy level can be found experimentally using a navigation task. In our experiment, the robot navigates through a set of simulated worlds with human input being generated by a software component. The results are used to find appropriate autonomy levels for three example missions and a subsequent user study. Y1 - 2008 U6 - https://doi.org/10.1109/ROBOT.2008.4543524 SP - 2146 EP - 2151 PB - IEEE ER -